As the number of channels available to television viewers has increased, along with the diversity of the programming content available on such channels, it has become increasingly challenging for television viewers to identify television programs of interest. Electronic program guides (EPGs) identify available television programs, for example, by title, time, date and channel, and facilitate the identification of programs of interest by sorting or searching the available television programs in accordance with personalized preferences.
A number of recommendation tools have been proposed or suggested for recommending television programming and other items of interest. Television program recommendation tools, for example, apply viewer preferences to an EPG to obtain a set of recommended programs that may be of interest to a particular viewer. Generally, television program recommendation tools obtain the viewer preferences using implicit or explicit techniques, or using some combination of the foregoing. Implicit television program recommendation tools generate television program recommendations based on information derived from the viewing history of the viewer, in a non-obtrusive manner. Explicit television program recommendation tools, on the other hand, explicitly question viewers about their preferences for program attributes, such as title, genre, actors, channel and date/time, to derive viewer profiles and generate recommendations.
When selecting an item of interest, individuals are often influenced by the selections made by others. For example, people who are viewed as “trendsetters” often influence the viewing or purchase habits of others. Online retailers, such as Amazon.com, employ collaborative filtering techniques to recommend additional items to a customer based on selections made by other people who purchased the same item. Thus, following the purchase of a product, a customer is often advised that other customers who purchased this product also purchased certain other products.
In addition, many individuals often wish that they had watched a television program that was watched by a friend or colleague. There is currently no mechanism, however, to recommend television programs or other items of interest based on recommendations made to a selected third party, such as a friend, colleague or trendsetter. In addition, there is currently no mechanism for a plurality of recommenders to share recommendations and generate recommendation scores based on information about what other recommenders are recommending.